Book Review — Remote Sensing of the Terrestrial Water Cycle

2017 ◽  
Vol 83 (5) ◽  
pp. 335-336
Author(s):  
MelissaJ. Rura
Keyword(s):  
Remote Sensing ◽  
Water Cycle ◽  
Terrestrial Water

Self-validating deep learning of continental hydrology through satellite gravimetry and altimetry

2021 ◽  
Author(s):  
Christopher Irrgang ◽  
Jan Saynisch-Wagner ◽  
Robert Dill ◽  
Eva Boergens ◽  
Maik Thomas
Keyword(s):  
Neural Network ◽  
Satellite Altimetry ◽  
Water Cycle ◽  
Global Water Cycle ◽  
Network Training ◽  
The Neural Network ◽  
Continental Hydrology ◽  
Terrestrial Water ◽  
Combine Machine ◽  
Groundwater Basins

<p>Space-borne observations of terrestrial water storage (TWS) are an essential ingredient for understanding the Earth's global water cycle, its susceptibility to climate change, and for risk assessments of ecosystems, agriculture, and water management. However, the complex distribution of water masses in rivers, lakes, or groundwater basins remains elusive in coarse-resolution gravimetry observations. We combine machine learning, numerical modeling, and satellite altimetry to build and train a downscaling neural network that recovers simulated TWS from synthetic space-borne gravity observations. The neural network is designed to adapt and validate its training progress by considering independent satellite altimetry records. We show that the neural network can accurately derive TWS anomalies in 2019 after being trained over the years 2003 to 2018. Specifically for validated regions in the Amazonas, we highlight that the neural network can outperform the numerical hydrology model used in the network training.</p><p>https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020GL089258</p>

Plants, vital players in the terrestrial water cycle

2021 ◽  
Author(s):  
Marie-Claire ten Veldhuis ◽  
Tom van den Berg ◽  
Martine van der Ploeg ◽  
Elias Kaiser ◽  
Satadal Dutta ◽  
...  
Keyword(s):  
Water Transport ◽  
Water Cycle ◽  
Turgor Pressure ◽  
Water Dynamics ◽  
Plant Water ◽  
Flow Monitoring ◽  
Vital Functions ◽  
Main Challenge ◽  
Macro Scale ◽  
Terrestrial Water

<p>Plant transpiration accounts for about half of all terrestrial evaporation (Jasechko et al., 2013). Plants need water for many vital functions including nutrient uptake, growth, maintenance of cell turgor pressure and leaf cooling. Due to the regulation of water transport by stomata in the leaves, plants lose 97% of the water they take via their roots, to the atmosphere. They can be viewed as transpiration-powered pumps on the interface between the soil and atmosphere.</p><p>Measuring plant-water dynamics is essential to gain better insight into their role in the terrestrial water cycle and plant productivity. It can be measured at different levels of integration, from the single cell micro-scale to the ecosystem macro-scale, on time scales from minutes to months. In this contribution, we give an overview of state-of-the-art techniques for transpiration measurement and highlight several promising innovations for monitoring plant-water relations. Some of the techniques we will cover include stomata imaging by microscopy, gas exchange for stomatal conductance and transpiration monitoring, thermometry for water stress detection, sap flow monitoring, hyperspectral imaging, ultrasound spectroscopy, accelerometry, scintillometry and satellite-remote sensing.</p><p>Outlook: To fully assess water transport within the soil-plant-atmosphere continuum, a variety of techniques is required to monitor environmental variables in combination with biological responses at different scales. Yet this is not sufficient: to truly solve for spatial heterogeneity as well as temporal variability, dense network sampling is needed.</p><p>In PLANTENNA (https://www.4tu.nl/plantenna/en/) a team of electronics, precision and microsystems engineers together with plant and environmental scientists develop and implement innovative (3D-)sensor networks that measure plant and environmental parameters at high resolution and low cost. Our main challenge for in-situ sensor autonomy (“plug and forget”) is energy: we want the sensor nodes to be hyper-efficient and rely fully on (miniaturised) energy-harvesting.</p><p><strong>REFERENCES: </strong></p><p>Jasechko, S., Sharp, Z. D., Gibson, J. J., Birks, S. J., Yi, Y., & Fawcett, P. J. (2013). Terrestrial water fluxes dominated by transpiration. Nature, 496(7445), 347-350.<br>Plantenna: "Internet of Plants". (n.d.). https://www.4tu.nl/plantenna/en/</p><p> </p>

scholarly journals Diagnosing hydrological limitations of a land surface model: application of JULES to a deep-groundwater chalk basin

2016 ◽  
Vol 20 (1) ◽  
pp. 143-159 ◽  
Author(s):  
N. Le Vine ◽  
A. Butler ◽  
N. McIntyre ◽  
C. Jackson
Keyword(s):  
Land Surface ◽  
Expert Knowledge ◽  
Water Cycle ◽  
Land Surface Model ◽  
Surface Model ◽  
Deep Groundwater ◽  
Resolution Model ◽  
Terrestrial Water ◽  
Water Redistribution ◽  
Types Of Information

Abstract. Land surface models (LSMs) are prospective starting points to develop a global hyper-resolution model of the terrestrial water, energy, and biogeochemical cycles. However, there are some fundamental limitations of LSMs related to how meaningfully hydrological fluxes and stores are represented. A diagnostic approach to model evaluation and improvement is taken here that exploits hydrological expert knowledge to detect LSM inadequacies through consideration of the major behavioural functions of a hydrological system: overall water balance, vertical water redistribution in the unsaturated zone, temporal water redistribution, and spatial water redistribution over the catchment's groundwater and surface-water systems. Three types of information are utilized to improve the model's hydrology: (a) observations, (b) information about expected response from regionalized data, and (c) information from an independent physics-based model. The study considers the JULES (Joint UK Land Environmental Simulator) LSM applied to a deep-groundwater chalk catchment in the UK. The diagnosed hydrological limitations and the proposed ways to address them are indicative of the challenges faced while transitioning to a global high resolution model of the water cycle.

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